Cardiovascular disease is the leading cause of death in both men and women in the United States. Over 16 million Americans have coronary heart disease (CHD), causing about 0.5 million deaths each year. The most common CHD is coronary artery disease which is mainly caused by atherosclerosis. Clinical evidence in recent years shows that noncalcified plaques (NCPs) are more vulnerable to rupture than calcified plaques. Plaque rupture and the thrombosis that follows is the main cause of acute myocardial infarction. Multidetector coronary CT angiography (cCTA) has the potential to help clinicians in early detection and in quantification of NCPs. cCTA may thus be useful for CHD detection, risk stratification, monitoring, and evaluation of the effectiveness of risk reduction treatment. However, many of these potential applications have not been utilized clinically. The goal of this project is to develop a computer-aided detection (CADe) system to serve as a second reader for assisting clinicians in detection and quantification of NCPs in cCTA exams. Our specific aims are to (1) develop machine learning methods for detection of NCPs causing stenosis and/or positive remodeling along coronary arteries, and (2) evaluate the effect of CADe on radiologists' detection of NCPs on cCTA by observer ROC study. To achieve these aims, we will collect a database of cCTA cases for training and testing the CADe system, define the search space by designing 3D multiscale coronary artery response enhancement, segmentation, and dynamic balloon vessel tracking methods, develop a unique vessel- stitching method to automatically identify the best-quality phase for each individual artery segment from all available phases in prospectively or retrospectively gated cCTA exams, develop innovative vessel-sector- profile analysis and vessel lumen analysis to detect NCPs that cause stenosis or positive remodeling, estimate the total NCP volume, and explore calibration method to quantify plaque density by phantom studies. To demonstrate the usefulness of CADe, a preclinical reader study will be conducted to compare radiologists' detection accuracy of NCPs with and without CADe. The major innovations of this project include (1) being the first CADe system to automatically detect non-calcified plaques including those cause positive remodeling or stenosis in cCTA, (2) development of new machine learning techniques including the vessel-stitching method, vessel-sector-profile analysis, multiscale enhancement response, and dynamic balloon tracking specifically suited for coronary arterial trees, and (3) conducting the first ROC study to evaluate the effect of CADe on radiologists' detection of NCPs.